Improving the Synaptic Behavior with Polar Orthorhombic Phase in Hf0.5Zr0.5O2 Film
Source abstract
Abstract
As a high-κ dielectric material, hafnium oxide has excellent potential for applications in the fields of both memristor and ferroelectric materials. It is now mostly believed that the ferroelectricity of hafnium oxide films originates from the polar orthorhombic phase. The effect of this polar orthorhombic phase on the memristor properties of the hafnium oxide films is still uncertain. In this work, the proportion of the orthorhombic phase in the films was influenced by varying the preparation parameters, and the microstructure was further determined by microscopic characterization. High cooling rate favors the orthorhombic phase and more uniform microstructure, and the multiresistance state and synaptic behavior of the HZO film was investigated, which shows that the HZO film in the orthorhombic phase has good potential in memristor applications.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Ferroelectric and Negative Capacitance Devices · Advanced Memory and Neural Computing · Semiconductor materials and devices
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics, Sun Yat-sen University, Guangzhou 510275, P. R. China
State Key Laboratory of Optoelectronic Materials and Technologies, School of Physics, Sun Yat-sen University, Guangzhou 510275, P. R. China
Centre for Physical Mechanics and Biophysics, School of Physics, Sun Yat-sen University, Guangzhou 510275, P. R. China
Funding
National Natural Science Foundation of China · Natural Science Foundation of Guangdong Province · Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices
References (56)
Show source-provided references
1. 10.1021/nl904092h10.1021/nl904092h ↗
2. 10.1038/s41928-017-0006-810.1038/s41928-017-0006-8 ↗
3. 10.1002/adma.20160431010.1002/adma.201604310 ↗
4. 10.1038/nmat485610.1038/nmat4856 ↗
5. 10.1021/acsaelm.9b0069410.1021/acsaelm.9b00694 ↗
6. 10.1021/am400728710.1021/am4007287 ↗
7. 10.1016/j.nanoen.2019.10426810.1016/j.nanoen.2019.104268 ↗
8. 10.1038/s41928-018-0069-110.1038/s41928-018-0069-1 ↗
9. 10.1007/s10854-015-4248-910.1007/s10854-015-4248-9 ↗
10. 10.1016/j.mtphys.2021.10039210.1016/j.mtphys.2021.100392 ↗
11. 10.1002/adma.20180288310.1002/adma.201802883 ↗
12. 10.1021/acsaelm.0c0009410.1021/acsaelm.0c00094 ↗
13. 10.1142/S2010135X1630003610.1142/S2010135X16300036 ↗
14. 10.1063/1.492118210.1063/1.4921182 ↗
15. 10.1063/1.494572710.1063/1.4945727 ↗
16. 10.1088/1361-6528/ab348010.1088/1361-6528/ab3480 ↗
17. 10.1038/srep2575710.1038/srep25757 ↗
18. 10.1002/admt.20210132310.1002/admt.202101323 ↗
19. 10.1063/1.511821710.1063/1.5118217 ↗
20. 10.1002/smll.20210757510.1002/smll.202107575 ↗
21. 10.1063/1.363405210.1063/1.3634052 ↗
22. 10.1126/science.abf378910.1126/science.abf3789 ↗
23. 10.1039/D1NR01260F10.1039/D1NR01260F ↗
24. 10.1080/00150193.2020.179165810.1080/00150193.2020.1791658 ↗
25. 10.1063/1.491913510.1063/1.4919135 ↗
26. 10.7567/APEX.9.09150110.7567/APEX.9.091501 ↗
27. 10.1007/s10854-019-00874-410.1007/s10854-019-00874-4 ↗
28. 10.1126/science.aba006710.1126/science.aba0067 ↗
29. 10.1007/BF0054160110.1007/BF00541601 ↗
30. 10.1103/PhysRevB.90.06411110.1103/PhysRevB.90.064111 ↗
31. 10.1021/acs.jpcc.9b0410610.1021/acs.jpcc.9b04106 ↗
32. 10.1021/nl302049k10.1021/nl302049k ↗
33. 10.1038/s41563-018-0196-010.1038/s41563-018-0196-0 ↗
34. 10.1063/1.504171510.1063/1.5041715 ↗
35. 10.1063/1.498406810.1063/1.4984068 ↗
36. 10.1039/D0TC05853J10.1039/D0TC05853J ↗
37. 10.1063/1.481148310.1063/1.4811483 ↗
38. 10.20517/microstructures.2021.1110.20517/microstructures.2021.11 ↗
39. 10.1103/PhysRevLett.127.08760210.1103/PhysRevLett.127.087602 ↗
40. 10.1021/acsaelm.8b0006510.1021/acsaelm.8b00065 ↗
41. 10.1021/acsaelm.9b0025610.1021/acsaelm.9b00256 ↗
42. 10.1063/1.504171510.1063/1.5041715 ↗
43. 10.1038/s41699-021-00229-w10.1038/s41699-021-00229-w ↗
44. 10.1063/5.005865610.1063/5.0058656 ↗
45. 10.1002/aelm.20180052210.1002/aelm.201800522 ↗
46. 10.1021/acsami.2c0315110.1021/acsami.2c03151 ↗
47. 10.1016/j.apmt.2022.10158710.1016/j.apmt.2022.101587 ↗
48. 10.1002/adfm.20180603710.1002/adfm.201806037 ↗
49. 10.1021/acs.cgd.0c0009510.1021/acs.cgd.0c00095 ↗
50. 10.1002/aelm.20160017310.1002/aelm.201600173 ↗
51. 10.1002/aelm.20197005410.1002/aelm.201970054 ↗
52. 10.1007/s40843-020-1444-110.1007/s40843-020-1444-1 ↗
53. 10.1021/acsaelm.2c0118610.1021/acsaelm.2c01186 ↗
54. 10.1039/C8NR04734K10.1039/C8NR04734K ↗
55. 10.1002/pssr.20210007210.1002/pssr.202100072 ↗
56. Bache, M. L. K. UCI Machine Learning Repository, 2022. https://archive.ics.uci.edu (accessed June, 2022).